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README.md
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# GPT-Grug-125m
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A finetuned version of [GPT-Neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the 'grug' dataset.
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# Training Procedure
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This was trained on the 'grug' dataset, using the "HappyTransformers" library on Google Colab.
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This model was trained for 4 epochs with learning rate 1e-2.
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# Biases & Limitations
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This likely contains the same biases and limitations as the original GPT-Neo-125M that it is based on, and additionally heavy biases from the grug datasets.
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# Intended Use
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This model is meant for fun, please do not take anything this caveman says seriously.
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# GPT-Grug-125m
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A finetuned version of [GPT-Neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the 'grug' dataset.
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A demo is available [here](https://huggingface.co/spaces/DarwinAnim8or/grug-125m-chat)
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# Training Procedure
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This was trained on the 'grug' dataset, using the "HappyTransformers" library on Google Colab.
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This model was trained for 4 epochs with learning rate 1e-2.
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The notebook used to train has been included in this repo.
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# Biases & Limitations
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This likely contains the same biases and limitations as the original GPT-Neo-125M that it is based on, and additionally heavy biases from the grug datasets.
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# Intended Use
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This model is meant for fun, please do not take anything this caveman says seriously.
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# Sample Use
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```python
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#Import model:
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from happytransformer import HappyGeneration
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happy_gen = HappyGeneration("GPT-NEO", "DarwinAnim8or/gpt-grug-125m
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#Set generation settings:
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from happytransformer import GENSettings
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args_top_k = GENSettings(no_repeat_ngram_size=3, do_sample=True,top_k=50, temperature=0.7, max_length=50, early_stopping=False)")
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#Generate a response:
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result = happy_gen.generate_text("""Person: "Hello grug"
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Grug: "hello person"
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Person: "how are you grug"
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Grug: "grug doing ok. grug find many berry. good for tribe."
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###
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Person: "what does grug think of new spear weapon?"
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Grug: "grug no like new spear weapon. grug stick bigger. spear too small, break easy"
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###
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Person: "what does grug think of football?"
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Grug: \"""", args=args_top_k)
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print(result)
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print(result.text)
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```
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